关键词: 抓取区域检测, SENet结构, 平衡特征金字塔, 实时检测

Abstract:

Aiming at the problem that the current robot target grasping area detection method cannot take into account the detection accuracy and real-time performance, a real-time detection method of robot target grasping area based on SE-RetinaGrasp neural network model is proposed. Firstly, based on the one-stage target detection model RetinaNet, the position of the grasping rectangles and the grasping angle are extracted. Secondly, the SENet structure is used to determine the important feature channel in the grabbing detection task. Then, combined with the balanced feature pyramid design idea, the fusion is fully integrated. The feature information of the upper and lower layers is used to enhance the detection performance of the small grasping rectangles. Finally, the experimental verification is performed on the Cornell dataset. The results show that the method improves the detection accuracy while improving the detection accuracy, efficiency to meet real-time detection requirements.

Key words: grasping area detection, SENet structure, balanced feature pyramid, real-time detection